Fault diagnosis of transformer winding short circuit based on WKPCA-WM and IPOA-CNN

When the winding of the power transformer is short-circuited, the winding will experience constant vibration, which will cause axial instability of the winding, and then lead to winding looseness, deformation, bulge, etc., therefore, a diagnosis method based on the Improved Pelican Optimization Algorithm and Convolutional Neural Network (IPOA-CNN) for short-circuit voiceprint signal of transformer windings is proposed. At the same time, considering the input parameter dimension of deep learning cannot be too high, a new feature parameter selection method is constructed for this model. Firstly, the frequency characteristics of winding acoustic vibration signals are analyzed, and then the characteristic parameters of transformer acoustic signals are extracted by Wavelet Packet Energy Spectrum (WPES) and Mel Frequency Cepstrum Coefficient (MFCC), respectively. Then, the two methods are combined to construct the WM feature extraction algorithm, and the Weighted Kernel Principal Component Analysis (WKPCA) is used to reduce the dimension of the feature to obtain the feature parameters with accurate feature information and low redundancy; Finally, combined with Sobol sequence to optimize the initial population of Pelican Optimization Algorithm (POA), the convolution kernel of Convolutional neural network (CNN) was optimized by IPOA, and the optimal convolution kernel was obtained. The transformer winding short-circuits voiceprint diagnosis models of WKPCA-WM and IPOA-CNN were constructed, which realized the accurate diagnosis of winding short-circuit voiceprint. The validity and feasibility of the method are verified by the acoustic signal data collected in the laboratory.